ATAC-seq signal processing and recurrent neural networks can identify RNA polymerase activity

Tripodi, I. J.; Chowdhury, M.; Dowell, R. D.

doi:10.1101/531517

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bioRxiv · 10.1101/531517

ATAC-seq signal processing and recurrent neural networks can identify RNA polymerase activity

Tripodi, I. J.·Chowdhury, M.·Dowell, R. D.

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Abstract

Nascent transcription assays are the current gold standard for identifying regions of active transcription, including markers for functional transcription factor (TF) binding. Here we present a signal processing-based model to determine regions of active transcription genome-wide using the simpler assay for transposase-accessible chromatin, followed by high-throughput sequencing (ATAC-seq). The focus of this study is twofold: First, we perform a frequency space analysis of the "signal" generated from ATAC-seq experiments short reads, at a single-nucleotide resolution, using a discrete wavelet transform. Second, we explore different uses of neural networks to combine this signal with its underlying genome sequence in order to classify ATAC-seq peaks on the presence or absence of bidirectional transcription. We analyze the performance of different data encoding schemes and machine learning architectures, and show how a hybrid signal/sequence representation classified using recurrent neural networks (RNNs) yields the best performance across different cell types. Contactrobin.dowell@colorado.edu

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Tripodi, I. J., Chowdhury, M., Dowell, R. D.. 2019-01-26. ATAC-seq signal processing and recurrent neural networks can identify RNA polymerase activity. https://doi.org/10.1101/531517

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